Earticle

초록

한국어

This study investigates the use of machine learning techniques to estimate the value of studio apartments (Officetel), which are increasingly important as combined office and residential spaces for single-person households and freelancers. It aims to identify key variables affecting studio apartment prices and preprocess them for accurate predictions. Transaction data from studio apartments are used to compare the predictive performance of four methods: Multiple Regression Analysis, Random Forest, XG Boosting, and Deep Learning. The study seeks to determine the best-performing models for price estimation and aims to develop predictive models applicable to various real estate types and regions.

목차

Abstract
I. INTRODUCTION
II. TRENDS IN REAL ESTATE AUTOMATED VALUATION MODELS
III. MODEL CONSTRUCTION AND PERFORMANCE ANALYSIS
A. Data Collection and Preprocessing
B. Comparison of Predictive Models and Model Training
IV. CONCLUSIONS AND IMPLICATIONS
ACKNOWLEDGMENT
REFERENCES

저자

• Jinhyung Cho [ Department of AI Software Dongyang Mirae University ]
• Kyutae Park [ Division of Software Development AION Corporation ]
• Yoorim Cho [ Division of Software Development AION Corporation ]

참고문헌